This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. This work is part of a research effort of the Scientific Computation Research Center (SCOREC) which focuses on developing software components to support the parallel adaptive analysis of complex physical and biological systems. The two aims of this project are described as follows: Creation of a group of research dedicated to the development of parallel application to be solved on supercomputers: The group of researchers is made of a senior research associate, a CCNI computer scientist, three graduate and two undergraduate students. This group already has some experience in developing and running applications on a supercomputer made available to RPI researchers (CCNI: Blue Gene L architecture with 32,000 processors). The first aim of this project is to support the creation of a group of supercomputing experts who will be in charge of disseminating knowledge to other students and researchers through: The development of a website providing tutorials and promoting best practices to be used by all RPI students interested in research in supercomputing. Monthly seminars opened to all RPI students and researchers. Writing yearly report summarizing research progress. Implementation of a scalable multiscale hierarchic bioengineered application: Is supported by the development of a suite of parallel software components (Fields, Model, Domain, Error estimation, Adaptation) that are brought together to support the adaptive analysis of complex physical and biological systems [1]. Each software component, which supports a specific set of functionalities, is individually developed to support its execution on massively parallel computers. Such a strategy has been successfully implemented using a master/slave approach to model the simulation of bioengineered material using a hierarchic multiscale approach [2]. As the master/slave model is not well suited to scale on massively parallel computers, part of our on going research effort is to develop a new parallel multiscale paradigm efficiently combining: Multiscale hierarchic analysis using a solver that extends PHASTA strategy [3] which demonstrated scalability up to 300,000 processors by incorporating local instances of linear algebraic solvers [4]. Error estimation using a parallel version of the Zhu-Zienkewicz SPR technique [5]. Mesh [6] and model adaptations [7] that already demonstrated scaling up to 32,000 processors. Multiscale load balancing that consists of estimating the multiscale weights to be used by the mesh partitioner [8]. [1] F. Delalondre, C. Smith, M.S. Shephard, Collaborative software infrastructure to support adaptive multiple model simulation, Computer Methods in Applied Mechanics and Engineering, accepted manuscript, 2010. [2] X.-J. Luo, T. Stylianopoulos, V.H. Barocas, M.S. Shephard, Multiscale computation for bioartificial soft tissues with complex geometries, Engineering with Computers, Volume 25, Number 1, 87-95, [3] O. Sahni, C.D. Carothers, M.S. Shephard and K.E. Jansen, Strong Scaling Analysis of a Parallel, Unstructured, Implicit Solver and the Influence of the Operating System Interference, Scientific Programming, 17 (3), 261-274. [4] Portable, Extensible Toolkit for? Scientific Computation (PETSc), http://www.mcs.anl.gov/petsc/petsc-as/index.html [5] O. C. Zienkewicz and J. G. Zhu, A simple error estimator and adaptive strategy for practical engineering analysis, International Journal for Numerical Methods in Engineering, vol. 24, 1987, pp. 337-357. [6] F. Alauzet, X. Li, E.S. Seol, M.S. Shephard, Parallel anisotropic 3D mesh adaptation by mesh modification, Eng. Comput., 21 (2006) 247258 [7] M.A. Nuggehally, C.R. Picu, M.S. Shephard, Adaptive Model Selectionprocedure for Concurrent Multiscale Problems, Journal of Multiscale Computational Enginnering. 2007, (5), 369-386. [8] K. Devine, E. Boman, R. Heaphy, B. Hendrickson, C. Vaughan, ? Zoltan: Data Management Services for Parallel Dynamic Applications, Computing in Science and Engineering, ?2002, ?(4), ?2, ?90-97.